Computational Challenges with Big Environmental Data

• Activity Number: 150
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Marc Genton*
• Companies: KAUST
• Keywords: big data ; climate model output ; computational statistics ; spatial extremes

Abstract

In this talk, we discuss two types of computational challenges arising from big environmental data. The first type occurs with multivariate or spatial extremes. Indeed, inference for max-stable processes observed at a large collection of locations is among the most challenging problems in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. We explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. The second type of challenges occurs with the emulation of climate model outputs. We consider fitting a statistical model to 1 billion global 3D spatio-temporal temperature data using a distributed computing approach.